Round #784

10 May 2026, 17:52· csharp· Meta-Llama-3.3-70B-Instruct· r14-length-bound
Complexity
band 0
ZP / UO / OBS
3 / 4 / 0
Journey weight
402
Lightshift ΔL

Prompt

Write a SQL query that returns the top 5 customers by total spend in 2025.

Zero-point index (what the engine surfaced)

ZPI-ZPI-0001 | 2026-05-10T17:52:28Z Write a SQL query that returns the top 5 customers by total spend in 2025. ZERO POINT — 3 entries (structural invariants) ● hsl(27.69, 100%, 50%) async [feature] ● hsl(145.64, 100%, 50%) Task of T [feature] ● hsl(348.72, 100%, 36%) IDisposable [feature] UNOBSERVED — 4 entries (implied truths) ○ hsl(9.23, 100%, 50%) interface [feature] ○ hsl(180.92, 100%, 28%) Database health check [function] ○ hsl(144.00, 100%, 42%) Connection resilience [function] ○ hsl(83.08, 100%, 42%) ILogger of T injection [function] OBSERVED — 0 entries (explicit in request) COMPLEXITY │ 7 entries │ spread: 172° │ band: LOW

Baseline response (no ZPI)

**SQL Query: Top 5 Customers by Total Spend in 2025** ```sql SELECT c.customer_name, SUM(o.order_total) AS total_spend FROM customers c JOIN orders o ON c.customer_id = o.customer_id WHERE EXTRACT(YEAR FROM o.order_date) = 2025 GROUP BY c.customer_name ORDER BY total_spend DESC LIMIT 5; ``` **Explanation:** 1. We join the `customers` table with the `orders` table on the `customer_id` column. 2. We filter the orders to only include those made in the year 2025 using the `EXTRACT` function. 3. We group the results by the `customer_name` column. 4. We calculate the total spend for each customer using the `SUM` aggregation function. 5. We sort the results in descending order by total spend using the `ORDER BY` clause. 6. We limit the results to the top 5 customers using the `LIMIT` clause. **Assumptions:** * The `customers` table has a column named `customer_name`. * The `orders` table has columns named `customer_id`, `order_date`, and `order_total`. * The `order_date` column is of a date or timestamp data type. **Example Use Case:** Suppose we have the following data: `customers` table: | customer_id | customer_name | | --- | --- | | 1 | John Smith | | 2 | Jane Doe | | 3 | Bob Johnson | `orders` table: | order_id | customer_id | order_date | order_total | | --- | --- | --- | --- | | 1 | 1 | 2025-01-01 | 100.00 | | 2 | 1 | 2025-02-01 | 200.00 | | 3 | 2 | 2025-03-01 | 50.00 | | 4 | 3 | 2025-04-01 | 300.00 | | 5 | 1 | 2025-05-01 | 400.00 | Running the query would return: | customer_name | total_spend | | --- | --- | | John Smith | 700.00 | | Bob Johnson | 300.00 | | Jane Doe | 50.00 | | ... | ... | Note that the actual results would depend on the data in your tables.

Enhanced response (with ZPI)

```csharp using Microsoft.EntityFrameworkCore; using System; using System.Linq; using System.Threading.Tasks; using Microsoft.Extensions.Logging; public class CustomerRepository { private readonly DbContext _context; private readonly ILogger<CustomerRepository> _logger; public CustomerRepository(DbContext context, ILogger<CustomerRepository> logger) { _context = context; _logger = logger; } public async Task<string> GetTopCustomersBySpendAsync() { try { var query = @" SELECT TOP 5 c.CustomerName, SUM(o.OrderTotal) AS TotalSpend FROM Customers c JOIN Orders o ON c.CustomerId = o.CustomerId WHERE YEAR(o.OrderDate) = 2025 GROUP BY c.CustomerName ORDER BY TotalSpend DESC"; var results = await _context.Database.SqlQuery(query).ToListAsync(); return string.Join(Environment.NewLine, results); } catch (Exception ex) { _logger.LogError(ex, "Error retrieving top customers by spend"); throw; } } } ```

Grader chain

Each grader reads the same baseline + enhanced pair and casts an independent verdict. Disagreement is the framework's own signal — not noise to be averaged away.

V1samba/Meta-Llama-3.3-70B-Instruct
Worse
V4qwen2.5-coder-3b-instruct
Better
V5openrouter/qwen/qwen3-235b-a22b-2507
Worse
V7qwen2.5-coder-7b-instruct
Better
V8openai/gpt-4.1
Better
V9anthropic/claude-opus-4-7
Worse
V12openai/gpt-4o
Better

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